March 21, 2023
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This algorithm just like in classical neural networks for interpolation builds a depth map and selects individual objects on it. But it draws only those parts of the frames on which the movement of objects occurs. Thanks to the revision of the standard approach the developers manage to achieve dizzying success according to them RIFE works times and in some cases up to times faster than its preecessors. Do it yourself interpolation So what do we nee to do our own interpolation with RIFE. First of all we nee sufficient computing power. As a stand I chose a cloud server with an NVIDIA Tesla T video card.

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Cloud servers with GPU Power in resource intensive tasks fromĀ  hour After that we go to the official GitHub repository of the authors of the RIFE neural network and clone it to our server git clone git github megvii research ECCV RIFE cd ECCV RIFE Next for the correct operation of the neural network we nee to install the necessary dependencies. They are in the requirements file. We execute the command pip install r requirements A traine model Fax Lists is also require for the algorithm to work. You can teach yourself or use the model that the RIFE developers kindly traine for us. You can download the model here . The downloade model must be move to the train log . Further everything is very simple. We upload the video we nee to the server. After the download is complete you can start magic.

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To create a video with double the frame rate run the command python inference video py exp video video The process has begun You can go to drink tea or turn on the next episode of your favorite series if the video is long interpolation using neural networks BM Leads is a rather long process. For example a full run of Ghost in the Shell took me two and a half hours. When the algorithm finishes processing the video the finishe video with an increase number of frames will appear in the catalog that we downloade from GitHub. Let’s look at the results of our work On the left is the original recording while the video on the right is the result of RIFE’s work.

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